TL;DR

Data Scientist (Fraud Detection): Develop and deploy advanced machine learning, deep learning, and AI models to detect and prevent fraudulent activity in mobile attribution and marketing analytics. Focus on mining massive data sets, designing end-to-end ML pipelines, and collaborating with cross-functional teams to improve fraud detection capabilities.

Company

%hirify_global% is a cutting-edge technology company specializing in mobile attribution and marketing analytics, operating at massive scale with advanced AI-driven fraud detection solutions.

What you will do

Mine massive data sets to uncover fraud patterns and prevention opportunities.
Develop and productionize large-scale ML, DL, and AI models to detect and prevent fraudulent activity.
Translate business and product needs into scientific, explainable, and high-impact solutions.
Design and maintain end-to-end ML pipelines from data ingestion to production monitoring.
Collaborate closely with product, engineering, and business teams to drive data-driven decisions.
Stay current with AI and ML research and evaluate new technologies to strengthen fraud detection algorithms.

Requirements

M.Sc in Computer Science, Mathematics, Statistics, Engineering or related field
4+ years of experience in applied Data Science or Algorithmic Development in large-scale production environments
Hands-on experience training and fine-tuning ML, DL, and AI models
Strong programming skills in Python and proficiency in SQL
Solid understanding of data pipelines, MLOps practices, and working with huge datasets
Ability to mentor, collaborate, and communicate complex ideas clearly.

Nice to have

Experience in ad-tech or fraud detection systems.
Familiarity with LLMs, generative AI, or representation learning for anomaly detection.
Proven track record with large-scale distributed data systems.
Employee referral from %hirify_global%.

Culture & Benefits

Global company with 20 offices worldwide promoting diversity and equal opportunity.
Focus on providing teams with tools and resources to succeed.
Encourages curiosity, diversity, and innovation.